Format

Send to

Choose Destination
Nature. 2014 Mar 6;507(7490):57-61. doi: 10.1038/nature13087. Epub 2014 Feb 26.

A predictive fitness model for influenza.

Author information

1
1] Institute for Theoretical Physics, University of Cologne, Zülpicher Strasse 77, 50937 Köln, Germany [2] Biological Sciences, Columbia University, 607D Fairchild Center, New York, New York 10027, USA.
2
Institute for Theoretical Physics, University of Cologne, Zülpicher Strasse 77, 50937 Köln, Germany.

Abstract

The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

PMID:
24572367
DOI:
10.1038/nature13087
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center